ubuntu安装显卡驱动和cuda教程

1. 卸载原始的驱动

#查看安装的包
apt list --installed|grep -i nvidia
#卸载包
apt-get purge nvidia*

2. 下载新显卡驱动

https://www.nvidia.cn/download/index.aspx?lang=cn

复制下载链接，在系统中用wget下载

#下载
wget https://cn.download.nvidia.cn/tesla/470.57.02/nvidia-linux-x86_64-470.57.02.run
#安装
sudo sh nvidia-linux-x86_64-470.57.02.run

2.1 安装显卡驱动

3 安装cuda

官网链接

选择cuda版本，要和驱动的cuda版本一致

wget https://developer.nvidia.com/compute/cuda/10.0/prod/local_installers/cuda_10.0.130_410.48_linux
sudo sh cuda_10.0.130_410.48_linux

添加环境变量，将上图中的建议加到.bashrc文件中

please make sure that

          path includes /usr/local/cuda-11.4/bin

          ld_library_path includes /usr/local/cuda-11.4/lib64, or,

                   add /usr/local/cuda-11.4/lib64 to /etc/ld.so.conf and run ldconfig as root

vim ~/.bashrc
#添加路径
export path=$path:/usr/local/cuda-11.4/bin
export ld_library_path=$ld_library_path:/usr/local/cuda-11.4/lib64
#使环境生效
source ~/.bashrc

查看nvcc -v

cudatoolkit

sudo apt install nvidia-cuda-toolkit

4. 安装cudnn

安装cudnn

https://developer.nvidia.com/rdp/cudnn-download

wget https://developer.download.nvidia.cn/compute/machine-learning/cudnn/secure/8.2.2/11.4_07062021/ubuntu18_04-x64/libcudnn8_8.2.2.26-1%2bcuda11.4_amd64.deb?ajllhxbzztwe4iizwf68uvg1s73kk4kkbgqv6b0uko9hhnohosghlyo1br5cwc0naijlmc6c5sklyqbdqqdzboadcvqgbtmwkxjxigr7rouexd0vikuum57ukwmp3buqgr6sq4kkgnrrtuj5mjt
dpkg -i libcudnn8_8.2.2.26-1+cuda11.4_amd64.deb

5. 安装anaconda

wget https://mirror.tuna.tsinghua.edu.cn/anaconda/archive/anaconda3-2021.05-linux-x86_64.sh

添加环境变量

vim ~/.bashrc
export path="/usr/local/anaconda3/bin:$path"
source ~/.bashrc

替换anaconda源

"""更换清华conda源"""
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/

查看tensorflow版本

pip install tensorflow-gpu==2.2.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

测试安装的tensorflow

import tensorflow as tf
print(tf.test.is_gpu_available())
tf.__version__
tf.__path__

上述报错原因是cuda版本太高了，要选择10.1版本

上述报错原因是cudnn版本太高了，要选择7.6.5版本

默认python2调整为python3

apt-get install python3.7
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 100
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 150

sudo apt install python3-pip

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